rnn_tagger

RNN based tagger.

class hanlp.components.taggers.rnn_tagger.RNNTagger(**kwargs)[source]

An old-school tagger using non-contextualized embeddings and RNNs as context layer.

Parameters

**kwargs – Predefined config.

build_dataloader(data, batch_size, shuffle, device, logger=None, **kwargs) → torch.utils.data.dataloader.DataLoader[source]

Build dataloader for training, dev and test sets. It’s suggested to build vocabs in this method if they are not built yet.

Parameters
  • data – Data representing samples, which can be a path or a list of samples.

  • batch_size – Number of samples per batch.

  • shuffle – Whether to shuffle this dataloader.

  • device – Device tensors should be loaded onto.

  • logger – Logger for reporting some message if dataloader takes a long time or if vocabs has to be built.

  • **kwargs – Arguments from **self.config.

build_model(rnn_input, rnn_hidden, drop, crf, **kwargs) → torch.nn.modules.module.Module[source]

Build model.

Parameters
  • trainingTrue if called during training.

  • **kwargs**self.config.

build_vocabs(dataset, logger)[source]

Override this method to build vocabs.

Parameters
  • trn – Training set.

  • logger – Logger for reporting progress.

execute_training_loop(trn: torch.utils.data.dataloader.DataLoader, dev: torch.utils.data.dataloader.DataLoader, epochs, criterion, optimizer, metric, save_dir, logger, patience, **kwargs)[source]

Implement this to run training loop.

Parameters
  • trn – Training set.

  • dev – Development set.

  • epochs – Number of epochs.

  • criterion – Loss function.

  • optimizer – Optimizer(s).

  • metric – Metric(s)

  • save_dir – The directory to save this component.

  • logger – Logger for reporting progress.

  • devices – Devices this component and dataloader will live on.

  • ratio_width – The width of dataset size measured in number of characters. Used for logger to align messages.

  • **kwargs – Other hyper-parameters passed from sub-class.

fit(trn_data, dev_data, save_dir, batch_size=50, epochs=100, embed=100, rnn_input=None, rnn_hidden=256, drop=0.5, lr=0.001, patience=10, crf=True, optimizer='adam', token_key='token', tagging_scheme=None, anneal_factor: float = 0.5, anneal_patience=2, devices=None, logger=None, verbose=True, **kwargs)[source]

Fit to data, triggers the training procedure. For training set and dev set, they shall be local or remote files.

Parameters
  • trn_data – Training set.

  • dev_data – Development set.

  • save_dir – The directory to save trained component.

  • batch_size – The number of samples in a batch.

  • epochs – Number of epochs.

  • devices – Devices this component will live on.

  • logger – Any logging.Logger instance.

  • seed – Random seed to reproduce this training.

  • finetuneTrue to load from save_dir instead of creating a randomly initialized component. str to specify a different save_dir to load from.

  • eval_trn – Evaluate training set after each update. This can slow down the training but provides a quick diagnostic for debugging.

  • _device_placeholderTrue to create a placeholder tensor which triggers PyTorch to occupy devices so other components won’t take these devices as first choices.

  • **kwargs – Hyperparameters used by sub-classes.

Returns

Any results sub-classes would like to return. Usually the best metrics on training set.

fit_dataloader(trn: torch.utils.data.dataloader.DataLoader, criterion, optimizer, metric, logger: logging.Logger, ratio_width=None, **kwargs)[source]

Fit onto a dataloader.

Parameters
  • trn – Training set.

  • criterion – Loss function.

  • optimizer – Optimizer.

  • metric – Metric(s).

  • logger – Logger for reporting progress.

  • **kwargs – Other hyper-parameters passed from sub-class.